Everyone knows that AI is changing every industry. But a lot of marketers don’t see the full vision of how they could be using it. They’re getting comfortable using AI to generate draft text but not realizing the full potential of everything else AI could do for them.
That’s why it’s useful to rely on models created by industry experts to better understand the full potential of AI. Professor Thomas H. Davenport and Deloitte Consulting principal Rajeev Ronanki outline a full model for understanding the business applications of AI in this article in the Harvard Business Review.
The Davenport-Ronanki model outlining AI describes not just what kinds of business applications it has but also how to apply them. Many marketing leaders aren’t using this type of structured thinking to build their AI content strategies, so let’s dive in to give it a better look!
The Three Types of AI in the Davenport-Ronanki Model
Even though Davenport and Ronanki outlined their model in 2018, its validity is proven by how much it’s still relevant even after all the recent advances in AI we’ve seen. Here are the three types of AI they describe:
Process automation AI, also known as Robotic Process Automation (RPA), refers to the automation of digital and physical tasks, primarily back-office administrative and financial activities, using software robots or AI workers. It differs from the other types of AI—cognitive insights and cognitive engagement—because it focuses on executing tasks and processes rather than analyzing data or interacting with users.
RPA is especially useful because of its immediate impact on efficiency and cost reduction, as it can quickly take over repetitive, rule-based tasks that don’t require complex decision-making or learning. It’s the least expensive and easiest to implement among the AI types discussed in the article and typically brings a quick and high return on investment. However, it’s also noted as the least “smart” in terms of cognitive abilities, as these systems are initially not programmed to learn and improve, although they are gradually being enhanced with more intelligence.
Examples of how companies use process automation AI for content marketing include:
- SEO Management: AI can automatically update meta-tags, track and fix broken links, and adjust content to align with the latest SEO trends and analytics insights. Depending on your needs, these boring, repetitive tasks can save you hours while improving your content’s performance.
- Automated Content Distribution: AI can automate the process of content distribution across multiple platforms. It can schedule and post content on social media, update website content, and ensure that new articles or posts are shared with relevant stakeholders and syndicated on appropriate channels at optimal times.
- Dynamic Content Optimization: RPA systems can continuously A/B test different headlines, images, and calls to action in content pieces to determine which combinations perform the best, automatically implementing the most successful elements in real time to improve engagement and conversion rates.
These applications are particularly well-suited to working across multiple back-end systems, allowing companies to streamline processes that are essential to their content creation and distribution.
Cognitive insight AI refers to applications that employ algorithms to parse large data sets and identify patterns, providing interpretations that can be used for more accurate predictions, personalized experiences, and strategic decision-making. This type of AI differs from process automation in that it goes beyond executing pre-programmed tasks to actively learning and improving from data over time, often using machine learning or deep learning.
In content marketing, companies can harness cognitive insight AI in several ways:
- Predictive Analytics for Content Trends: Cognitive insight AI can analyze consumer behavior and social media trends to predict upcoming content trends. This helps you create content that aligns with future interests, staying ahead of the curve.
- Sentiment Analysis: AI tools can assess social media and other content for sentiment, providing insights into how audiences feel about a brand or topic. This can inform the tone and direction of new marketing content to better resonate with the audience.
- Content Performance Forecasting: By analyzing historical content performance data, cognitive insight AI can forecast the potential success of future content pieces, helping you to invest efforts in the most promising topics and formats.
These cognitive insight applications enable content marketers to leverage deep data-driven insights, enhancing the strategic aspects of content creation and distribution for more targeted, relevant, and effective marketing efforts.
Cognitive engagement AI involves technologies that interact with users in a natural, human-like way. This type differs from both process automation and cognitive insight because it focuses on direct interaction with customers or employees, providing services like answering questions, offering recommendations, or providing other forms of assistance.
Companies can use cognitive engagement AI to improve their content marketing in a few ways:
- Chatbots and Virtual Assistants: Cognitive engagement AI can be used to deploy AI-powered chatbots on websites and social media platforms to provide instant, interactive communication with customers. These can deliver personalized content recommendations, answer FAQs, or guide users through the marketing funnel.
- Interactive Content: AI can be used to create quizzes, polls, or interactive videos that adapt to user inputs, which can increase engagement and time spent with the brand while also collecting user preferences and feedback.
- Customer Feedback Analysis: AI can analyze customer feedback across various channels to understand sentiments, extract key themes, and identify content gaps that need to be filled to improve customer satisfaction and engagement.
Cognitive engagement AI tools help brands foster a more personalized and interactive relationship with their audience, enhancing the customer experience and improving the effectiveness of content marketing strategies.
How to Use Process Automation AI for Content Creation
Process automation AI can significantly streamline SEO management by automating repetitive and labor-intensive tasks. You can use that technology to improve your content optimization and distribution, saving you time and making sure you get consistent results. Here are a few ways you can use it:
With process automation AI, you can manage the SEO side of your content production with a lot less hassle. For example, automation can conduct keyword research at scale. By crawling through vast datasets, AI can identify trending keywords, long-tail phrases, and semantic variations that are relevant to your niche.
Similarly, AI-powered tools can automate on-page SEO tasks. They can scan your website’s content to ensure that it aligns with best practices for keyword density, meta tags, and header tags. They can also suggest improvements and even implement them on CMS platforms that support automation.
Another critical area is backlink analysis. AI can continuously monitor your backlink profile, alerting you to any changes, such as new backlinks or the loss of existing ones, and can assess the quality of these links to manage your link-building efforts effectively.
Lastly, process automation tools can track your rankings across different search engines and generate detailed reports, highlighting progress and identifying areas that require attention, thus allowing you to make data-driven decisions quickly.
By automating these processes, SEO professionals can focus on strategic planning and creative aspects of SEO that require human insight, thus improving overall SEO performance and efficiency.
Content distribution is one of the most powerful applications of process automation AI. That’s because content distribution is one of the most important parts of the content creation process. Previously, we’ve been known to make some pretty bold statements about how much time and money you should spend on content distribution. But what if we’re wrong?
Just look at this chart below.
Do you really need to be spending 75% of your budget on content distribution?
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